Project: Identify Customer Segments¶
In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.
This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks. The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.
It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. There will be times in the project where you will need to make and justify your own decisions on how to treat the data. These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.
At the end of most sections, there will be a Markdown cell labeled Discussion. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.
# import libraries here; add more as necessary
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time
from sklearn.decomposition import PCA
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
# magic word for producing visualizations in notebook
%matplotlib inline
'''
Import note: The classroom currently uses sklearn version 0.19.
If you need to use an imputer, it is available in sklearn.preprocessing.Imputer,
instead of sklearn.impute as in newer versions of sklearn.
'''
Step 0: Load the Data¶
There are four files associated with this project (not including this one):
Udacity_AZDIAS_Subset.csv: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).Udacity_CUSTOMERS_Subset.csv: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).Data_Dictionary.md: Detailed information file about the features in the provided datasets.AZDIAS_Feature_Summary.csv: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns
Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.
To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the .csv data files in this project: they're semicolon (;) delimited, so you'll need an additional argument in your read_csv() call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.
Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings.
# Load in the general demographics data.
azdias = pd.read_csv("Udacity_AZDIAS_Subset.csv", sep = ';')
# Load in the feature summary file.
feat_info = pd.read_csv("AZDIAS_Feature_Summary.csv", sep = ';')
# Add options for data frame - may need later on a large dataset
pd.set_option("display.max_rows", None)
# Check the structure of the data after it's loaded (e.g. print the number of
# rows and columns, print the first few rows).
print(f'The Azdias Subset has {azdias.shape[0]} rows and {azdias.shape[1]} columns \n')
azdias.head(5)
azdias.info()
print(f'The Azdias Feature Summary has {feat_info.shape[0]} rows and {feat_info.shape[1]} columns')
feat_info.head(5)
feat_info.info()
Step 1: Preprocessing¶
Step 1.1: Assess Missing Data¶
The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the Discussion cell with your findings and decisions at the end of each step that has one!
Step 1.1.1: Convert Missing Value Codes to NaNs¶
The fourth column of the feature attributes summary (loaded in above as feat_info) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. [-1,0]), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.
As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.
# Identify missing or unknown data values and convert them to NaNs.
# First, find out how many missing values are in the data
azdias.isna().sum()
# Take a quick look at missing data totals
print(f'There are {azdias.shape[0]*azdias.shape[1]} values in the data frame. Of those, {azdias.isna().sum().sum()} are \
missing or blank. This represents {((azdias.isna().sum().sum())/(azdias.shape[0]*azdias.shape[1]))*100:.2f}% of the data.')
# Clean the missing data by changing those values into NaN. First copy data frame to new for data integrity.
feat_info.missing_or_unknown.unique()
# Create copies of datasets to maintain original data integrity
clean_azdias = azdias.copy()
clean_feat_info = feat_info.copy()
for column in clean_azdias.columns:
miss_unknown_array = feat_info[feat_info.attribute == column].missing_or_unknown.to_string().split()[1].strip('][').split(',')
if miss_unknown_array[0] and 'XX' not in miss_unknown_array and 'X' not in miss_unknown_array:
miss_unknown_array = np.array(list(map(int, miss_unknown_array)))
clean_azdias = clean_azdias.replace({column:miss_unknown_array}, np.nan)
elif miss_unknown_array[0]:
clean_azdias = clean_azdias.replace({column:miss_unknown_array}, np.nan)
print(f'As seen above, there are {clean_azdias.shape[0]*clean_azdias.shape[1]} values in the data frame. \
After replacing values, {clean_azdias.isna().sum().sum()} are still missing.')
print(f'This represents {((clean_azdias.isna().sum().sum())/(clean_azdias.shape[0]*clean_azdias.shape[1]))*100:.2f}% \
of the total values.')
Step 1.1.2: Assess Missing Data in Each Column¶
How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's hist() function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)
For the remaining features, are there any patterns in which columns have, or share, missing data?
# Perform an assessment of how much missing data there is in each column of the
# dataset.
clean_azdias.isna().sum().sort_values()
# Investigate patterns in the amount of missing data in each column.
missing_data = clean_azdias.isna().sum()/clean_azdias.shape[0]*100
plt.hist(missing_data)
plt.xlabel('Percentage of missing data(%)')
plt.ylabel('Counts')
plt.title('Columns containing missing data')
plt.show();
# Show all columns with missing data, giving the percentages
missing_data.sort_values().plot.barh(figsize=(15,30))
plt.xlabel('Percentage of missing data(%)')
plt.ylabel('Column Names')
plt.title('Columns with missing data')
plt.grid(True)
plt.show();
# List which columns are above 15% missing data.
for column in clean_azdias.columns[clean_azdias.isna().sum()/clean_azdias.shape[0]*100 > 30]:
print(f"Column {column} has NaNs percentage of {clean_azdias[column].isna().sum()/clean_azdias.shape[0]*100:.2f}%")
# Remove the outlier columns from the dataset. (You'll perform other data
# engineering tasks such as re-encoding and imputation later.)
print("The below columns are outliers.")
cols_to_remove = ['AGER_TYP', 'GEBURTSJAHR', 'TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP', 'KBA05_BAUMAX']
for col_name in cols_to_remove:
if col_name in clean_azdias.columns:
clean_azdias.drop(col_name, axis = 1, inplace = True)
clean_feat_info.drop(clean_feat_info[clean_feat_info['attribute']==col_name].index[0], axis=0, inplace=True)
print(f'Removing column {col_name} from clean_azdias and cleaned_feat_info datasets.')
else:
print(f'Column {col_name} not found in data frame. Skipping.')
# Fix due to issue in customer data
cols_to_remove2 = ['KKK', 'REGIOTYP']
for col_name in cols_to_remove2:
if col_name in clean_azdias.columns:
clean_azdias.drop(col_name, axis=1, inplace=True)
clean_feat_info.drop(clean_feat_info[clean_feat_info['attribute']==col_name].index[0], axis=0, inplace=True)
print(f'Also removing column {col_name} from clean_azdias and cleaned_feat_info datasets.')
else:
print(f'Column {col_name} not found in data frame. Skipping.')
Discussion 1.1.2: Assess Missing Data in Each Column¶
Are there any patterns in missing values?¶
The most obvious pattern is that several columns are missing large amounts of data. The majority of columns are missing 15% or less, but the columns listed below surpassed the 15% threshold - most of them by a large amount.
Which columns were removed from the dataset?¶
The columns that have been removed from the dataset are:
- AGER_TYP
- GEBURTSJAHR
- TITEL_KZ
- ALTER_HH
- KK_KUNDENTYP
- KBA05_BAUMAX
These columns were above the 30% threshold I determined to hold the most outliers in the full dataset.
Additionally, the 2 columns below were removed due to a conflict in the customer data set:
- KKK
- REGIOTYP
Step 1.1.3: Assess Missing Data in Each Row¶
Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.
In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.
- You can use seaborn's
countplot()function to create a bar chart of code frequencies and matplotlib'ssubplot()function to put bar charts for the two subplots side by side. - To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.
Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.
# How much data is missing in each row of the dataset?
plt.hist(clean_azdias.isnull().sum(axis=1), bins=50)
plt.xlabel('Percentage of missing row data(%)')
plt.ylabel('Count')
plt.title('Rows with missing data')
plt.grid(True)
plt.show();
# Write code to divide the data into two subsets based on the number of missing
# values in each row.
#Check counts first
missingRowCounts = clean_azdias.isnull().sum(axis=1)
missingRowCounts.sort_values(ascending=False)